Towards HMM-based glissando detection for recordings of Chinese bamboo flute

Abstract

Playing techniques such as ornamentations and articulation effects constitute important aspects of music performance. However, their computational analysis is still under-explored due to a lack of data and established methods. Focusing on the Chinese bamboo flute, we introduce a two-stage glissando detection system based on hidden Markov models (HMMs) with Gaussian mixtures. A rule-based segmentation process extracts glissando candidates that are consecutive note changes in the same direction. Glissandi are then identified by two HMMs (glissando and non-glissando). The study uses a newly created dataset of Chinese bamboo flute recordings. The results, based on both frame- and segment-based evaluation, achieve F-measures of 78% and 73% for ascending glissandi, and 65% and 72% for descending glissandi, respectively. The dataset and method can be used for performance analysis.